OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies
OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies
This research presents OmniGuide, a flexible framework designed to improve Vision-language-action (VLA) model performance on complex tasks requiring spatial understanding or precise manipulation. By expressing various guidance sources as differentiable energy functions with task-specific attractors and repellers in 3D space, the system integrates complementary strengths from sources like 3D foundation models and semantic-reasoning VLMs. Extensive experiments across simulation and real-world environments demonstrate that OmniGuide significantly enhances the success and safety rates of state-of-the-art generalist policies, including π0.5 and GR00T N1.6, matching or surpassing prior methods that incorporate specific guidance sources.
The paper introduces OmniGuide, a framework that integrates diverse guidance sources like 3D foundation models and semantic VLMs into Vision-language-action (VLA) policies via differentiable energy functions; experiments in simulation and real-world settings report significant improvements in success and safety rates for state-of-the-art generalist policies such as π0.5 and GR00T N1.6.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Source：arXiv Robotics — research abstracts · arxiv.org